Features¶

Feature extractors are used, as the name suggests, to extract features from a signal. AutonFeat provides a wide range of feature extractors that can be used to extract features from a signal. The following sections describe the various feature extractors that are available in AutonFeat.

Domain Agnostic¶

Domain agnostic features are applicable to most signals irrespective of the domain.

Summary Statistics¶

Data Sparsity Measures¶

Information Theoretic Measures¶

Domain Specific¶

Domain expertise almost always helps in extracting better features. AutonFeat provides a wide range of domain specific features that can be used to extract features from a signal. The following sections describe the various domain specific feature extractors that are available in AutonFeat.

Biomedical, and Physiological Signals¶

(Coming Soon)

Functional Form¶

A functional form for each of the transforms above is also provided for convenience. Check out the autonfeat.functional sub-module for more details.

Custom Featurizers¶

It is possible to design custom features while extending the functionality of AutonFeat. The Transform class is an abstraction representing a featurizer. When defining a custom featurizer, one can utilize the efficiency of the SlidingWindow abstraction by inhering defining the featurizer to inherit from Transform. The following example demonstrates how to create a custom feature that computes the mean of the signal.

import numpy as np
from typing import Callable, Union
from autonfeat.core import Transform

class MeanTransform(Transform):
    def __init__(self, name: str = "Mean") -> None:
        super().__init__(name=name)

    def __call__(self, signal_window: np.ndarray, where: Callable[[Union[int, float, np.int_, np.float_]], Union[bool, np.bool_]] = lambda x: not np.isnan(x)) -> Union[np.float_, np.int_]:
        where_fn = np.vectorize(pyfunc=where)
        return np.mean(x, where=where_fn(x))

This can then be passed as the featurizer to be applied at every sliding window interval as such -

import autonfeat as aft

# Random data
n_samples = 100
x = np.random.rand(n_samples)

# Create sliding window
ws = 10
ss = 10
window = aft.SlidingWindow(window_size=ws, step_size=ss)

# Create transform
tf = MeanTransform()

# Get featurizer
featurizer = window.use(tf)

# Get features
features = featurizer(x)

# Print features
print(window)
print(tf)
print(features)

See this for more examples on how to use feature extractors in AutonFeat.

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